DIGITAL IMAGE PROCESSING
Subject Code: (A70436) Regulations : R16 JNTUH
Class :IV Year B.Tech ECE I Semester
Department of Electronics and communication Engineering
BHARAT INSTITUTE OF ENGINEERING AND TECHNOLOGY
Ibrahimpatnam -501 510, Hyderabad
IV Yr-ECE – I Sem. 90
DIGITAL IMAGE PROCESSING (A70436)
COURSE PLANNER
I. COURSE OVERVIEW:
The students will be enlightened on digital image processing and to improve the appearance of an image to a human observer, to extract from image quantitative
information that is not readily apparent to the eye and to calibrate an image in photometric or geometric terms. Also the course provides an introduction to basic concepts and methodologies for
digital image processing and to develop a foundation that can be used as the basis for further study and research in this field.
II. PREREQUISITE:
1. Basics of Mathematics
2. Signals and systems
3. Digital signal processing.
III. COURSE OBJECTIVE:
IV. COURSE OUTCOME:
S.No Description Bloom’s Taxonomy Level
1
Students will be able to Explain the basic elements and
applications of image processing
Comprehension Understanding
(Level 2)
2 Students will be able to Analyze image sampling and
quantization requirements and implications
Analyze (Level 4)
3 Students will be able to Design and implement two-dimensional spatial and frequency filters for image
enhancement
Synthesis (Level 5)
4 Students will be able to Model and Demonstrate the image restoration problem in both time and frequency domains
Application (Level 3)
5 Students will be able to Explain the image segmentation
and image compression problem
Comprehension Understanding
(Level 2)
1. This course provides an understand Image fundamentals and techniques
2. This course build various Image enhancement, restoration and compression techniques
3. This course develop various Image segmentation methods, Wavelet based and
morphological Image Processing
4. This course give the student a taste of the applications of the theories taught in the subject. This will be achieved through the project and some selected lab sessions.
5. This course will introduce the students to some advanced topics in digital image processing
IV Yr-ECE – I Sem. 91
6 Students will be able to Develop & Illustrate
Morphological Image Processing.
Comprehension Understanding
(Level 2)
V. HOW PROGRAM OUTCOMES ARE ASSESSED:
PROGRAM OUTCOMES (PO) LEVEL PROFICIENCY
ASSESSED BY
PO1: Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex
engineering problems.
3 Assignments
PO2: Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems
reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
3 Exercises
PO3: Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs
with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
3 ------
PO4: Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and
interpretation of data, and synthesis of the information to provide valid conclusions.
3 ------
PO5: Modern tool usage: Create, select, and apply
appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an
understanding of the limitations.
3 Discussion, Seminars
PO6: The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health,
safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
3
Design exercise,
Prototypes
PO7: Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate
the knowledge of, and need for sustainable development.
2
Exercise, Seminars,
Discussions
PO8: Ethics: Apply ethical principles and commit to
professional ethics and responsibilities and norms of the engineering practice.
2 Discussions
IV Yr-ECE – I Sem. 92
VI. HOW PROGRAM SPECIFIC OUTCOMES ARE ASSESSED
Program Specific Outcomes LEVEL PROFICIENCY
ASSESSED BY
PSO1
Professional Skills: An ability to understand the basic
concepts in Electronics & Communication Engineering and
to apply them to various areas, like Electronics,
Communications, Signal processing, VLSI, Embedded
systems etc., in the design and implementation of complex
systems.
3 Lectures,
Assignments
PSO2
Problem-Solving Skills: An ability to solve complex
Electronics and communication Engineering problems, using
latest hardware and software tools, along with analytical
skills to arrive cost effective and appropriate solutions.
3 Lectures,
Assignments
PSO3
Successful Career and Entrepreneurship: An
understanding of social-awareness & environmental-wisdom
along with ethical responsibility to have a successful career
and to sustain passion and zeal for real-world applications
using optimal resources as an Entrepreneur.
3 Guest Lectures
VII. SYLLABUS:
UNIT I:
PO9: Individual and team work: Function effectively as an
individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
3 -----
PO10: Communication: Communicate effectively on complex
engineering activities with the engineering community and with society at large, such as, being able to
comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
2 Seminars,
Discussions
PO11: Project management and finance: Demonstrate
knowledge and understanding of the engineering and management principles and apply these to one‘s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
3 Workshops, Prototypes
PO12: Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and
life-long learning in the broadest context of technological change.
3
Seminar,
Discussions
IV Yr-ECE – I Sem. 93
Digital image fundamentals, Sampling and quantization, Relation ship between
pixels;
Image Transforms: 2-D FFT, Properties, Walsh Transform, Hadamard Transform,
Discrete Cosine Transform, Haar Transform, Slant Transform, Hotelling Transform
UNITV II:
Image Enhancement (spatial domain): Introduction, Enhancement in spatial
domain, Enhancement through point operations, Types of point Operations,
Histogram manipulation, Linear and non linear gray level transformation, local or
neighborhood operation , median filter, spatial domain high pass filtering
Image Enhancement (Frequency Domain): Filtering in frequency domain,
obtaining frequency domain filters from spatial filters, Generating filters directly in
the frequency domain, Low pass (smoothing) filters in frequency domain, high pass
(sharpening) filters in frequency domain
UNIT III:
Image Restoration: Degradation model, Algebraic approach to restoration, inverse
filtering, least mean square filters Constrained Least Squares Restoration, Interactive
Restoration
UNIT IV:
Image Segmentaton: Detection of discontinuities, Edge linking and boundary
detection, Thresholding, Region oriented segmentation.
Morphological Image Processing: Dilation, Structuring element decomposition, The
strel function, Erosion, Combining Dilation and Erosion, Opening and closing, The
hit or miss transformation,
UNIT V:
Image Compression:
Redundancies and their removal methods, Fidelity criteria, Image compression
models, Source encoder and decoder, Error free compression, Lossy compression,
Lossy and Lossless Predictive Coding, Transform Based Compression, JPEG 2000
Standards
TEXT BOOKS:
IV Yr-ECE – I Sem. 94
1. Digital Image Processing – Rafael .C. Gonzalez, Richard E Woods, Pearson
Education.
2. Digital Image Processing – S. Jayaraman, S. Esakkirajan, T. Veerakumar
REFERENCE BOOKS:
1. Digital Image Processing using MAT LAB, Rafael, C. Gonzalez, Richard E woods
and Stens L Eddings, 2nd Edn, TMH,2010
2. Fundamentals of Digital Image Processing, A.K. Jain, PHI, 1989
3. Digital Image Processing and Computer Vision, Somka, Hlavac, Boyle, Cengage
Learning (India Edition) 2008
4. Introductory Computer vision Imaging Techniques and Solutions, Adrain Low, 2Nd
Edn, 2008
5. Introduction to Image Processing & Analysis – John C. Russ, J. Christian Russ, CRC
Press, 2010
6. Wavelet Transforms (Introduction to theory and applications), Raghuveer M. Rao
and Ajit S. Bopardikar, Pearson, 2000
7. Digital image processing with matlab & labview – Vipula singh
VIII. COURSE PLAN (WEEK-WISE):
Lecturer
No.
Week Unit Topic to be covered Course Learning Outcomes Reference
1
WEEK-1 1
Introduction to subject and overview
Outline what is an image and what is an digital image processing
T1, T2
2 Fundamental steps in image processing with a block
diagram Discuss various image fundamentals
3 Basic concepts of digital image and image processing,
gray scale explanation, sampling and quantization
concepts Illustrate sampling and quantization
4 Elements of image processing, Relationship between pixels: Neighbor of a
pixel, Connectivity, Adjacency, Path, etc,.
Relate relation between pixels
5 Relationship of pixels
contd…Some basic operations on images
6 WEEK- Image transforms, Fourier Explain 2d fft properties
IV Yr-ECE – I Sem. 95
2 transform, one dimensional
and two dimensional transform, properties of 2D
FT: Separability, Translation, Periodicity and Conjugate symmetry
T1, T2
7 Average value, Distributivity and Scaling,
Evaluate the significance of the
transforms 8 Laplacian, Convolution and
Correlation, Sampling , The
Inverse FFT,
9 2D-Discrete Fourier transform
Demonstrate various 2-D transforms
10 2D-Walsh transform,
11 2D-Hadamard Transform
12
WEEK-3
Haar transform T1, T2
13 Slant Transform,
14 Hotelling transform and its significance
15 Discrete Cosine Transform (DCT)
16 Review of topics covered
17
WEEK-
4
2
Image Enhancement (spatial domain):
Enhancement by point
processing: simple intensity transformations-contrast
stretching, image negatives, log or power transformations,
How to enhance image in spatial domain
T1, T2
18 dynamic range compression, intensity or gray level slicing,
19 Enhancement by point processing contd...
Classify enhancement processing techniques
20 Histogram processing: histogram equalization
Analyze histogram manipulation 21 Histogram equalization
contd…
22 Histogram specification
23
WEEK-
5
Image subtraction, image
averaging
Illustrate local processing approaches T1, T2
Mock Test-I
Bridge class-I
24 Spatial Filtering: Smoothing filters, Sharpening filters
Illustrate filtering in spatial domain
IV Yr-ECE – I Sem. 96
25 Spatial Filtering: Smoothing
filters, Sharpening filters contd….
26
WEEK-
6
Image Enhancement
(Frequency Domain):
Enhancement in frequency
domain: Low pass filtering
Illustrate filtering in frequency domain
T1, T2
27 Enhancement in frequency domain: Low pass filtering contd…
28 High pass filtering, high boost
filtering and unsharp masking
29 WEEK-7
Homomorphic filtering
Bridge class-II
Bridge class-III
30
WEEK-
8
3
Degradation model:
degradation model for continuous functions, discrete formulation
Build degradation model
T1, T2
31 Algebraic approach to
restoration: Unconstrained Restoration
32 Constrained Restoration
33 Inverse filtering: Formulation,
Removal of Blur Caused by Uniform Linear Motion
34 Inverse filtering contd…. 35
Least Mean square (Wiener)
filter Model Least mean square filters
36
WEEK-9
Constrained Least squares Restoration
T1, T2
37 Interactive Restoration
Model Interactive restoration 38 Interactive Restoration
contd….. Bridge Class - V
Mock Test-II
39
WEEK-10
4
Detection of discontinuities:
point detection, line detection, edge detection Determine edges and boundary
T1, T2
40 Edge detection contd….. 41 Edge linking and Boundary
detection: Local processing
IV Yr-ECE – I Sem. 97
42 Global processing via Hough
transform
43
WEEK-11
Global Processing via Graph
theoretic approach
Determine edges by graph theoretic
method
T1, T2
44 Thresholding methods Design threshold models
45 Region based segmentation: basic formulation
Develop region segmentation 46 Region growing by pixel
aggregation
47 Region splitting and merging
48
WEEK-12
Morphological Image processing: Dilation and
Erosion approaches salient features Illustrate dilation, Erosion
T1, T2
49 Opening and closing
operations
50 Hit or Miss transformations
Bridge Class – VI
Bridge Class – VII
51
WEEK-13
5
Redundancies and their removal methods: coding
redundancy, interpixel redundancy, psychovisual
redundancy Classify various redundancies
T1, T2
52 Fidelity criteria
Develop compression models 53 Image compression models:
the source encoder and
decoder, the channel encoder and decoder
54 Error free compression: Huffman coding
Model Error free compression
55 Problems on Huffman coding
56
WEEK-14
Problems on Huffman coding T1, T2
57 Arithmetic coding
58 Problems on Arithmetic coding
59
WEEK-
15
Bit plane coding, run length
coding
Model Error free compression
T1, T2
60 Problems on run length coding
61 Lossless predictive coding
62 Lossy compression: Lossy predictive coding Model Error free compression
63 WEEK- Lossy predictive coding Model Lossy compression
IV Yr-ECE – I Sem. 98
64 16
Transform coding
Discuss Transform coding for
compression
65 JPEG 2000 standards Summarize JPEG 2000 Standards
Bridge Class – VIII
Bridge Class - IX IX. MAPPING COURSE OUTCOMES LEADING TO THE ACHIEVEMENT OF
PROGRAM OUTCOMES AND PROGRAM SPECIFIC OUTCOMES:
Course Outcomes
Program Outcomes Program Specific Outcomes
PO 1 PO 2 PO 3 PO 4 PO 5 PO6 PO7 PO8 PO 9 PO 10 PO 11 PO 12 PSO 1 PSO 2 PSO
3
1 3 3 - 1 1 3 3 1 - 1 3 1 1 1 1
2 - - - 2 - 3 - - 2- - - - 1 1 1
3 3 3 2 1 - 3 1 - 1 3 1 - - -
4 3 - - 2 1 - - - 2 - 3 1 1 1 1
5 - 3 - 1 - - 3 1 - 1 - 1 1 1 1
6 3 - - - 1 3 - 1 2 1 3 - - - -
AVG 2 1.5 0.34 0.5 0.67 1.5 1.5 0.67 1 0.67 2 0.67 0.67 0.67 0.67
X. QUESTION BANK (JNTUH)
UNIT I
Long Answer Questions
S. No Question
Blooms
taxonomy level
Course Outcomes
1 Explain the steps involved in digital image processing Understand 1
2 Discuss about the following relationships between pixels
with neat diagrams
Remember 1
i) Neighbours of a pixel
ii) Connectivity
iii) Distance measures
iv) Path
3 Write the expressions for Walsh transform kernel and Walsh
Remember 1
transform (1D &2D).
4 Briefly explain the forward and inverse transformation
kernels Understand 1
of image transforms
5 Name and explain some important properties of 2-D DFT Understand 1
6 Discuss about the Slant transform (1-D & 2-D) Remember 1
7 Discuss about the Hadamard transforms (1-D & 2-D) Remember 1
8 Discuss about the Haar transform (1-D & 2-D) Remember 1
9 Discuss about the Hotelling transforms (1-D & 2-D) Remember 1
IV Yr-ECE – I Sem. 99
10 State and prove separability property of 2D-DFT. Understand 1
11 State and prove the translation property Remember 1
12 State distributivity and scaling property Remember 1
Short Answer Questions
S. No Question
Blooms
taxonomy level
Course
Outcom
es
1 List the steps involved in digital image processing Understand 1
2 How do you represent the digital images? Remember 1
3 Explain about sampling and quantization of an image. Understand 1
4 Explain a simple Image formation model Understand 1
5
Name various arithmetic and logical operations that can
be done on Images Understand 1
6 What are the different fields in which Digital Image Processing is used? Remember 1
7
Explain about some of the geometrical operations that can be
done on images Understand 1
8 Distinguish between Fourier Magnitude Spectrum, Fourier Phase Spectrum and Power spectrum. Remember 1
9 Define discrete cosine transform Understand 1
10 Define an Image Understand 1
11 What is meant by pixel? Understand 1
12 Define Resolutions Remember 1
13 What is Dynamic Range? Understand 1
14 What is meant by illumination and reflectance? Remember 1
15 Find the number of bits required to store a 256 X 256 image with 32 gray levels Remember 1
16 Write the expression to find the number of bits to store a digital image? Understand 1
17 What is the need for transform? Understand 1
UNIT 2 Long Answer Questions
S. No Question
Blooms taxonomy
level
Course
Outcomes
1 Explain smoothing spatial filters and nonlinear order statistic spatial filters Understand 3
2 Explain about Prewitt and Sobel edge Detectors Remember 3
3 Describe image Histogram Equalization Remember 3
4 Explain the method of using the second derivate for Image sharpening by Laplacian Operator Remember 3
5 5.What is high boost spatial filtering? Compare it with high pass spatial filtering Understand 3
6 6.Discuss how the Bit Plane Slicing is useful in image Understand 3
IV Yr-ECE – I Sem. 100
processing
7
7.Discuss the importance of a kernel or mask or window in
spatial filtering used for enhancement of a digital image Analyze 3
8 How does the spatial filter with name Order static filter (non linear filter) or median filter work? Evaluate 3
9 What is meant by image enhancement by point processing?
Discuss any two methods in it. Remember 3
10 .Define histogram of a digital image. Explain how histogram is useful in image enhancement? Understand 3
11 . Write about Smoothing Spatial filters Understand 3
12 . What is meant by the Gradiant and the Laplacian? Discuss their role in image enhancement. Remember 3
13 .Description of Homo-morphic filtering Remember 3
14
. Expression for 2-D IHPF, Expression for BHPF, Expression for GHPF with sketches. Explain their usefulness
in Image enhancement Apply 3
15
. Give the expression for 2-D ILPF, BLPF & GLPF functions and sketch them. Explain their usefulness in Image enhancement Understand 3
16 . Expression for Butterworth Low Pass Filter in frequency domain and discuss Remember 3
17
. Compare the characteristics of Low pass, High pass and Homo-morphic filters in image enhancement in frequency
domain. Analyze 3
18 . Discuss about Ideal High Pass Filter and Butterworth High Pass filter Remember 3
Short Answer Questions
S. No Question
Blooms
taxonomy
level
Course
Outcomes
1 Narrate the concept of derivative filters. Understand 3
2
Discuss how the derivative filters are used in Digital Image
Enhancement? Remember 3
3 Describe Histogram Specification Understand 3
4 Explain Gray level transformation functions for contrast enhancement Remember 3
5 Discuss the Image negatives transformations Understand 3
6 Discuss the Contrast stretching transformations Understand 3
7 Explain the Local enhancement Understand 3
8 Explain the Image subtraction Apply 3
9 Explain the Image averaging Apply 3
10 What is the objective of image enhancement? Define spatial domain. Define point processing 3
11 Explain on procedure to derive frequency domain filtering from spatial domain Remember 3
IV Yr-ECE – I Sem. 101
12 Explain the method to set the cut off frequencies in ILPF? Analyze 3
13 Correspondence between filtering in the spatial & frequency domains Understand 3
14 Explanation on the basic steps for filtering used to enhance an image in frequency domain Understand 3
15 Explain the concept of homomorphism filtering Understand 3
UNIT 3
Long Answer Questions
S. No Question
Blooms taxonomy
level
Course
Outcomes
1 Explain the method of Least Mean Squares Filtering (Wiener) for image restoration
Understand 4
2 Explain model of image degradation/restoration process with a block diagram
Apply 4
3 Explain the method of Constrained Least Squares Filtering
for image restoration Understand 4
4 Explain three principle ways to estimate the degradation function for use in image restoration
Understand 4
5 Discuss the process of image restoration by direct inverse
filtering? Understand 4
6 Write about Noise Probability Density Functions for all noise models
Understand 4
Short Answer Questions
S. No Question
Blooms
taxonomy
level Course
Outcomes
1 Compare image enhancement and restoration techniques? Understand 4
2 Give the probability density functions for Rayleigh noise Remember 4
3 Give the probability density functions for the Erlang noise models
Remember 4
4 Give the probability density functions for Gaussian noise
models Remember 4
5 Give the probability density functions for Salt and Pepper noise models
Remember 4
UNIT 4
Long Answer Questions
S. No Question Blooms
taxonomy level Course
Outcomes
1 What are the derivative operators useful in image
segmentation? Explain their role in segmentation Understand 5
2 What is thresholding? Explain about global thresholding Remember 5
3 Explain about basic adaptive thresholding process used Understand 5
IV Yr-ECE – I Sem. 102
in image segmentation
4 Explain in detail the threshold selection based on boundary characteristics
Understand 5
5 Explain about region based segmentation Understand 5
6 What are the derivative operators useful in image
segmentation? Explain their role in segmentation Apply 5
7 Explain about the Global processing via the Hough Transform for edge linking
Apply 5
8 Explain about the Global processing via graph-theoretic
techniques for edge linking Understand 5
9 Explain about Region Splitting and Merging with an example
Apply 5
10
Write about the importance of Hit-or-Miss
morphological transformation operation on a digital binary image
Understand 6
11 Explain the opening operation in image morphology with
examples? Analyze 6
12 Explain the closing operation in image morphology withexamples?
Understand 6
13 Discuss the main steps involved in Continuous Wavelet Transform
Understand 6
Short Answer Questions
S. No Question
Blooms taxonomy
level
Course
Outcomes
1 Write about edge detection Remember 5
2 Explain about the Local processing for edge linking Understand 5
3 Write short note on Region Growing Remember 5
4 Write the mask for prewitt operator Remember 5
5 Write the mask for sobel operator Remember 5
6 Write the mask for laplacian operator Remember 5
7 Define segmentation Remember 5
8 Describe dilation morphological transformations on a binary image
Apply 6
9
Describe erosion morphological transformations on a binary
image Apply 6
10 Write short notes on Structuring elements in image morphological transformations
Understand 6
11 Write short notes on Hit-miss Transformation Understand 6
12 What are the Applications of morphology Remember 6
UNIT 5
Long Answer Questions
IV Yr-ECE – I Sem. 103
S. No Question
Blooms
taxonomy level
Course Outcomes
1 Explain about fidelity criterion Understand 5
2 Explain about image compression models Understand 5
3 Explain a method of generating variable length codes with
an example Understand 5
4 Explain arithmetic encoding process with an example Apply 5
5 Explain LZW coding with an example. Apply 5
6 Explain the concept of bit plane coding method Understand 5
7 Explain about lossless predictive coding Understand 5
8 Explain about lossy predictive coding Understand 5
9 Explain with a block diagram about transform coding system Understand 5
Short Answer Questions
S. No Question
Blooms
taxonomy
level
Course
Outcomes
1 How to calculate the memory required to store an image Understand 5
2 Define image compression Remember 5
3 What is image compression Remember 5
4 Explain Coding Redundancy Understand 5
5 Explain Interpixel Redundancy Understand 5
6 Explain Psychovisual Redundancy Understand 5
7 What are the characteristics of lossy compression Remember 5
8 What are the characteristics of lossless compression Remember 5
XI. Objective type questions:
UNIT-I
1 Image is defined as () a)2 d function b) 3 d function c) 1d function d) none
2 Image is a group of pixels 3 Walsh transform is used for image compression 4 8 bit image is also known as()
a) Color image b) B& W image c) gray level image d) none 5 Number of pixels present in MXN size image is()
a) M/N bits b)MN bits c) MN Kbytes d) none 6. Among the following image processing techniques which is fast, precise and flexible a) optical b) digital c) electronic d) photographic
7. An image is considered to be a function of a(x,y) where a represents a) height of image b) width of image c) amplitude of image d) resolution of image
8). Image negatives a gray level transformation is defined as: a. s=L-1-r b. s=L-r c. s=r-1-L d. none
Unit-II:
1. The relative frequency of occurrence of various gary levels present in an image is known as()
IV Yr-ECE – I Sem. 104
a) Bit plane b) pyramid c)histogram d) none 2. Smoothing filters also known as LPF
3. Sharpening filters also known as HPF 4. Image noise can be eliminated using()
a) HPF b) LPF c) BPF d) none
5. Which is the image processing technique used to improve the quality of image for human viewing?
a) Compression b) enhancement c) restoration d) analysis 6. Median filter eliminates salt and pepper noise 7. Which type of enhancement operations are used to modify pixel values according to the
value of the pixel‗s neighbors? a) point operations b) local operations c) global operations d) mask operations
8. What is spatial resolution? a)it is the largest discernible detail in an image b)it is the smallest discernible detail in an image c) a & b d) None
9. Image enhancement in frequency domain uses the following 2D DFT property() a)Scaling b) Rotation c)centering d) none
10. Smoothing filters also known as LPF 11. Homomorphic filter is used for contrast enhancement 12. Lapalcian filter also known as
a)HPF b) LPF c) BPF d) none 13. Max filter eliminates dark points
14. Which image processing technique is used to eliminate electronic noise by mathematical process?
a) Frame averaging b) Image understanding c) Image compression d) none
15. Frequency domain refers: a) Processing techniques are based on modifying the Fourier transform of an image
b. its processing techniques are based on modifying the laplace transform of an image. c) a & b d) None
Unit-III 1 Which of the following is a simple image restoration technique which eliminates
noise a)weighted restoration b) smoothing c)inverse d) none
2 Constrained least square restoration requires PSD
3 Inverse filtering is used for noise removal 4 Constrained least square restoration is a()
a)Weighted restoration b) non weighted restoration c) frequency domain d) none 5 Image degradation techniques removes blurring 6 Which is a fundamental task in image processing used to match two or more pictures?
a) Registration b) segmentation c) computer vision d) image differencing Unit-IV
1 Image segmentation uses the following opearators() a)Scaling b) Rotation c)derivative d) none
2 First order derivative operator is used for Edge detection
3 Graph theoretic approach is used for Edge linking
IV Yr-ECE – I Sem. 105
4 Region oriented segmentation includes a) Merging b) Splitting c) Both a and b d) none
5 Robert‘s operator is used for edge detection 1. What algorithm is used in fingerprint technology? a) Intensity based algorithm b) pattern based algorithm c) feature based
algorithm d) Recognition algorithm 2. In which technique which is used to determine changes between two images?
a) Image differencing b) segmentation c) skin texture analysis d) image differencing 3. Select one of the most appropriate applications of Computer vision? a) Medical computer imaging b) remote sensing c) geographical map d) medical diagnosis
4. The initial step in any image processing technique is a) Segmentation b) masking c) image acquisition d) normalization
5. Dilation-Morphological image operation technique is used to a) Shrink brighter areas of the image b) Diminishes intensity variation over the image
c) Expands brighter areas of the image d) Scales pixel intensity uniformly
6. Which technique is used for the images of the same scene are acquired from different viewpoints a) multiview analysis b) multitemporal analysis c) multisensory analysis d) image
differencing 7. Localization of iris, pupil, and eyelids come under
a) Normalization b) masking c) extraction d) segmentation 8. Morphological processing deals: a) With tools for extracting image components that are useful in the representation and
description of shape. b) With tools for changes in image components that are useful in the representation and
description of shape c) a & b d) None
Unit-V 1. Image compression is used for reducing the following parameter()
a) Size b) memory c)noise d) none 2. Lossy compression technique reduces the quality of the image
3. Image compression is
a) Making image look better b) Sharpening the intensity-transition regions
c) Minimizing degradation over image d) Reducing the redundancy of the image data 4. First application of digital image was in the:
a) News paper industry b) communication system c) a & b d) None of these 5. Which sensor is used for obtaining the video source in 3d face recognition system
a) Optical b) electronic c) 3d sensor d) 2d sensor XII. GATE QUESTIONS / UGC - NET:
DIP is not applicable for GATE and IES
XIII. WEBSITES:
IV Yr-ECE – I Sem. 106
1. www.imageprocessingplace.com
2. www.theiet.org.
XIV. EXPERT DETAILS:
XV. JOURNALS:
International:
1. IEEE Transactions on Pattern Analysis and Machine Intelligence,
ISSN:0162-8828 , Monthly. 2. IEEE Transactions on Image Processing, ISSN:1057-7149 ,
Monthly.
3. Computer Vision and Image Understanding, ISSN:1077-3142 , Monthly.
4. International Journal of Imaging Systems and Technology, ISSN:0899-9457 , Quarterly
National:
1. Journal of Image Processing.
2. Journal of Signal and Image Processing
XVI. LIST OF TOPICS FOR STUDENT SEMINARS:
1. Image enhancement for medical images.
2. Types of compression techniques.
3. Lossy and lossless compression
4. Transform coding method and wavelet method.
5. Morphological processing.
6. Edge detection techniques.
XVII. CASE STUDIES / SMALL PROJECTS:
1. Using MATLAB detection of tumor.
2. Implementation of speckle noise removal using various enhancement
techniques in medical and SAR images.
3. Implementation of image compression for medical images.
4. Implementation of wavelets for segmentation of images.
5. Processing color images using various histogram approaches.
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